ClassPractice10.R

install.packages("Deducer")
	install.packages("Rcmdr")
	

	library("Rcmdr")
	install.packages('rJava')
	

	setwd('C:/Users/Vatsal/Desktop/AMMA/Session 2 data_2017/')
	

	#R imports and stores the data in a data frame called "dt"
	dt<-read.csv("train.csv")
	#structure  of the data file imported
	str(dt)
	

	#Event Rate in the data
	sum(dt$Target)/nrow(dt)
	

	summary(dt)
	

	# subset into continous
	cont<-subset(dt,select=-c(id))
	

	#more detailed exploration
	z<-cont
	z<-cont
	for (i in 1:ncol(z)) 
	{
	  z1<-t(as.data.frame(quantile(z[,i],prob=c(0,0.05,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95,0.98,0.99,0.999,1),na.rm=T)))
	  row.names(z1)[1]<-colnames(z)[i]
	  total<-nrow(z)
	  total_miss<-sum(is.na(z[,i]))
	  z1<-cbind(z1,total,total_miss)
	  if (i==1) y<-z1 else y<-rbind(y,z1)
	}
	

	write.csv(y,"univ_cont.csv")
	

	

	

	library(psych)
	#library(help=psych)
	

	data(galton,package = "psych")
	head(galton)
	plot(galton$parent, galton$child)
	plot(galton$parent,
	     galton$child,
	     xlab = "Height of Parent",
	     ylab= "Height of Children",
	     main=" Relationship between Parent and Children Heights",
	     pch=17,
	     col="red")
	

	

	

	# ------------------- Time Series Plot or Line Chart -------------------------
	

	# Scenario - How Average Month Temprature is changing across years
	# nottem         Average Monthly Temperatures at Nottingham,1920-1939
	

	#library(help = "datasets")
	data(nottem,package = "datasets")
	head(nottem)
	plot(nottem)
	nottem
	

	# Add elements
	plot(nottem,
	     xlab="Years",
	     ylab="Avg Monthly Temp",
	     main="Temp across years",
	     col="blue",
	     type="l",
	     pch=20)
	

	# ------------------------  Frequency Distributions: Histogram -------------------
	

	# Distribution of Customer Age: How many customers are available across different age groups
	

	# Generate Age data
	## Generate a numeric vector for Age
	Age <- as.integer(rnorm(10000,m=55, sd=15))
	# histogram
	hist(Age)
	#?hist 
	hist(Age, breaks=50)
	

	# Add elements or beautify Histogram
	hist(Age, 
	     breaks=30,
	     col="green",
	     border="white", 
	     xlab="Age", 
	     ylab="Counts", 
	     main="Histogram:Age")
	

	# Scenario: Distribution of Mortality Rates
	

	#http://www.stats4stem.org/r-usmelanoma-data.html
	install.packages("HSAUR2")
	library(HSAUR2)
	data("USmelanoma")
	names(USmelanoma)
	

	xr <- range(USmelanoma$mortality) * c(0.9, 1.1)
	# Histogram
	hist(USmelanoma$mortality,
	     xlim = xr, 
	     xlab = "Mortality", 
	     main = "Histogram:Mortality", 
	     ylab = "Counts",
	     col="red",
	     border="yellow")
	

	# --------------  Box Plot : Distribution of a quantative/numeric Variable ----------------
	?boxplot
	

	# Box Plot
	boxplot(USmelanoma$mortality, 
	        ylim = xr, 
	        horizontal = TRUE, 
	        xlab = "Mortality")
	quantile(USmelanoma$mortality, probs = c(0, 0.25,0.5,0.75,1))
	

	table(USmelanoma$ocean)
	

	boxplot(mortality ~ ocean, 
	        data = USmelanoma, 
	        xlab = "Contiguity to an ocean", 
	        ylab = "Mortality")